Adaptive VIBE - Revision history

Jahn: /* Publications */

2009-12-11T02:19:20Z

‎Publications

Jahn: /* Publications */

2009-12-11T02:18:53Z

‎Publications

Jahn at 02:17, 11 December 2009

2009-12-11T02:17:42Z

Jahn at 02:16, 11 December 2009

2009-12-11T02:16:25Z

Jahn at 01:53, 11 December 2009

2009-12-11T01:53:31Z

Jahn at 01:52, 11 December 2009

2009-12-11T01:52:58Z

Jahn at 01:45, 11 December 2009

2009-12-11T01:45:31Z

Jahn: New page: In this system, a special type of the user model is supported called as a task model. It is aimed at supporting information analysts who are required to solve a specific task. A very short...

2009-12-11T01:45:03Z

New page: In this system, a special type of the user model is supported called as a task model. It is aimed at supporting information analysts who are required to solve a specific task. A very short...

New page

In this system, a special type of the user model is supported called as a task model. It is aimed at supporting information analysts who are required to solve a specific task. A very short term user model is constructed to model each different user task and personalization is made with the task models. A post filtering approach was applied to this purpose. The ad-hoc retrieval results triggered by user queries were re-ordered using the task model so that more relevant documents to the task model should be ranked up in the lists. In terms of the interface, we added a control with which users can control the effects of the query and the task model according to their needs and the context.
[[Image:TaskSieve.png|frame|center|TaskSieve system]]

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 <onlyinclude>
 * Ahn, J., and Brusilovsky, P. (2009) '''Adaptive Visualization of Search Results: Bringing User Models to Visual Analytics'''. Information Visualization 8 (3), 167-179.
-* Ahn, J. and Brusilovsky, P. (2007) '''From user query to user model and back: Adaptive relevance-based visualization for information foraging'''. In: T. Y. Lin, et al. (eds.) Proceedings of the 2007 international conference on Web Intelligence, WI '07, Silicon Valley, CA, USA, November 2-5, 2007, IEEE, pp. 706-712.
+* Ahn, J., and Brusilovsky, P. (2007) '''From user query to user model and back: Adaptive relevance-based visualization for information foraging'''. In: T. Y. Lin, et al. (eds.) Proceedings of the 2007 international conference on Web Intelligence, WI '07, Silicon Valley, CA, USA, November 2-5, 2007, IEEE, pp. 706-712.
 * Ahn, J.-w., Brusilovsky, P., and Sosnovsky, S. (2006) '''QuizVIBE: Accessing Educational Objects with Adaptive Relevance-Based Visualization'''. In: T. C. Reeves and S. F. Yamashita (eds.) Proceedings of World Conference on E-Learning, E-Learn 2006, Honolulu, HI, USA, October 13-17, 2006, AACE, pp. 2707-2714.
 * Brusilovsky, P., Ahn, J., Dumitriu, T., and Yudelson, M. (2006) '''Adaptive knowledge-based visualization for accessing educational examples'''. In: B. Ebad, et al. (eds.) Proceedings of Information Visualization, London, UK, July 5-7, 2006, IEEE, pp. 142-147
 </onlyinclude>

@@ Line 5: / Line 5: @@
 == Publications ==
 <onlyinclude>
-* Ahn, J., and Brusilovsky, P. '''Adaptive Visualization of Search Results: Bringing User Models to Visual Analytics'''. Accepted by Information Visualization, Special Issue on Human-Centered Information Visualization.
+* Ahn, J., and Brusilovsky, P. (2009) '''Adaptive Visualization of Search Results: Bringing User Models to Visual Analytics'''. Information Visualization 8 (3), 167-179.
 * Ahn, J. and Brusilovsky, P. (2007) '''From user query to user model and back: Adaptive relevance-based visualization for information foraging'''. In: T. Y. Lin, et al. (eds.) Proceedings of the 2007 international conference on Web Intelligence, WI '07, Silicon Valley, CA, USA, November 2-5, 2007, IEEE, pp. 706-712.
 * Ahn, J.-w., Brusilovsky, P., and Sosnovsky, S. (2006) '''QuizVIBE: Accessing Educational Objects with Adaptive Relevance-Based Visualization'''. In: T. C. Reeves and S. F. Yamashita (eds.) Proceedings of World Conference on E-Learning, E-Learn 2006, Honolulu, HI, USA, October 13-17, 2006, AACE, pp. 2707-2714.
 * Brusilovsky, P., Ahn, J., Dumitriu, T., and Yudelson, M. (2006) '''Adaptive knowledge-based visualization for accessing educational examples'''. In: B. Ebad, et al. (eds.) Proceedings of Information Visualization, London, UK, July 5-7, 2006, IEEE, pp. 142-147
 </onlyinclude>

@@ Line 4: / Line 4: @@
 == Publications ==
-</onlyinclude>
+<onlyinclude>
 * Ahn, J., and Brusilovsky, P. '''Adaptive Visualization of Search Results: Bringing User Models to Visual Analytics'''. Accepted by Information Visualization, Special Issue on Human-Centered Information Visualization.
 * Ahn, J. and Brusilovsky, P. (2007) '''From user query to user model and back: Adaptive relevance-based visualization for information foraging'''. In: T. Y. Lin, et al. (eds.) Proceedings of the 2007 international conference on Web Intelligence, WI '07, Silicon Valley, CA, USA, November 2-5, 2007, IEEE, pp. 706-712.

← Older revision		Revision as of 02:16, 11 December 2009
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	[[Image:VisualTaskSieve.png\|800px\|center\|Adaptive VIBE integrated into the TaskSieve system]]		[[Image:VisualTaskSieve.png\|800px\|center\|Adaptive VIBE integrated into the TaskSieve system]]
		+
		+	== Publications ==
		+	</onlyinclude>
		+	* Ahn, J., and Brusilovsky, P. '''Adaptive Visualization of Search Results: Bringing User Models to Visual Analytics'''. Accepted by Information Visualization, Special Issue on Human-Centered Information Visualization.
		+	* Ahn, J. and Brusilovsky, P. (2007) '''From user query to user model and back: Adaptive relevance-based visualization for information foraging'''. In: T. Y. Lin, et al. (eds.) Proceedings of the 2007 international conference on Web Intelligence, WI '07, Silicon Valley, CA, USA, November 2-5, 2007, IEEE, pp. 706-712.
		+	* Ahn, J.-w., Brusilovsky, P., and Sosnovsky, S. (2006) '''QuizVIBE: Accessing Educational Objects with Adaptive Relevance-Based Visualization'''. In: T. C. Reeves and S. F. Yamashita (eds.) Proceedings of World Conference on E-Learning, E-Learn 2006, Honolulu, HI, USA, October 13-17, 2006, AACE, pp. 2707-2714.
		+	* Brusilovsky, P., Ahn, J., Dumitriu, T., and Yudelson, M. (2006) '''Adaptive knowledge-based visualization for accessing educational examples'''. In: B. Ebad, et al. (eds.) Proceedings of Information Visualization, London, UK, July 5-7, 2006, IEEE, pp. 142-147
		+	</onlyinclude>

← Older revision		Revision as of 01:53, 11 December 2009
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	Adaptive VIBE is a variation of VIBE, a reference point-based spatial visualization algorithm. It supports the visualization of user models as well as usual documents. User model is displayed as a separate group of POIs (Points of Interests or reference points). Because it is located in distant positions from the query POIs, users can mediate visually between their explicit interests (queries) and the system estimated interests or search contents (user models).		Adaptive VIBE is a variation of VIBE, a reference point-based spatial visualization algorithm. It supports the visualization of user models as well as usual documents. User model is displayed as a separate group of POIs (Points of Interests or reference points). Because it is located in distant positions from the query POIs, users can mediate visually between their explicit interests (queries) and the system estimated interests or search contents (user models).

−	[[Image:VisualTaskSieve.png\|800px~~\|thumb~~\|center\|Adaptive VIBE integrated into the TaskSieve system]]	+	[[Image:VisualTaskSieve.png\|800px\|center\|Adaptive VIBE integrated into the TaskSieve system]]

← Older revision		Revision as of 01:52, 11 December 2009
Line 1:		Line 1:
−	~~In this system~~, a ~~special type~~ of ~~the~~ user model is ~~supported called~~ as a ~~task model. It is aimed at supporting information analysts who are required to solve a specific task~~. ~~A very short term user model~~ is constructed to model each different user task and personalization is made with the task models. A post filtering approach was applied to this purpose. The ad-hoc retrieval results triggered by user queries were re-ordered using the task model so that more relevant documents to the task model should be ranked up in the ~~lists. In terms of the interface~~, ~~we added a control with which~~ users can ~~control the effects of the query and the task model according to~~ their ~~needs~~ and the ~~context~~.	+	Adaptive VIBE is a variation of VIBE, a reference point-based spatial visualization algorithm. It supports the visualization of user models as well as usual documents. User model is displayed as a separate group of POIs (Points of Interests or reference points). Because it is located in distant positions from the query POIs, users can mediate visually between their explicit interests (queries) and the system estimated interests or search contents (user models).
−	[[Image:VisualTaskSieve.png\|~~frame~~\|center\|TaskSieve system]]	+
		+	[[Image:VisualTaskSieve.png\|800px\|thumb\|center\|Adaptive VIBE integrated into the TaskSieve system]]

← Older revision		Revision as of 01:45, 11 December 2009
Line 1:		Line 1:
	In this system, a special type of the user model is supported called as a task model. It is aimed at supporting information analysts who are required to solve a specific task. A very short term user model is constructed to model each different user task and personalization is made with the task models. A post filtering approach was applied to this purpose. The ad-hoc retrieval results triggered by user queries were re-ordered using the task model so that more relevant documents to the task model should be ranked up in the lists. In terms of the interface, we added a control with which users can control the effects of the query and the task model according to their needs and the context.		In this system, a special type of the user model is supported called as a task model. It is aimed at supporting information analysts who are required to solve a specific task. A very short term user model is constructed to model each different user task and personalization is made with the task models. A post filtering approach was applied to this purpose. The ad-hoc retrieval results triggered by user queries were re-ordered using the task model so that more relevant documents to the task model should be ranked up in the lists. In terms of the interface, we added a control with which users can control the effects of the query and the task model according to their needs and the context.
−	[[Image:~~TaskSieve~~.png\|frame\|center\|TaskSieve system]]	+	[[Image:VisualTaskSieve.png\|frame\|center\|TaskSieve system]]